Bibliographic record
Abstract
Traditionally, data published on the Web are available in HTML tables or CSV files, sacrificing much of its structure and semantics. As a result, Linked Data was proposed to publish and connect structured data on the Web. Linked Data enable individual entities which have selfdescribed feature in a particular format to be connected to related entities. Therefore, Linked Data applications are able to be connected within in global space and different domains without bounding. The practices of Linked Data has led to the extension of the Web with a global data space connecting data from diverse domains such as people, companies, books, scientific publications, music, genes, proteins, scientific data, location data, place names, point of interest, etc. Following the trend of publishing data on the Web as Linked Data, a number of researchers are working on the best practices of publishing sensor data as Linked Sensor Data [1, 2, 3]. The existing works use the XML serialization format of RDF and RDF/XML while it has been around for over a decade, there is very little uptake. In this paper, we are going to present a RESTful framework that publishes sensor data as Linked Sensor Data. We use the OGC Sensor Web for Internet of Things API as the fundamental framework, and extend it with the W3C JSON-Linked Data. We will demonstrate that the proposed framework is machine-readable, structured, lightweight, efficient to transmit over the network, with smaller memory footprint, and most importantly compatible to the Web principles. In this talk, we will also perform a live demonstration of the prototype system.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.006 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".